CN116911715A - Logistics identification and distribution robot based on artificial intelligence and control method thereof - Google Patents
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Abstract
The application provides a logistics recognition and distribution robot based on artificial intelligence and a control method thereof, which aim to solve the problem that a user needs to manually input a distribution address in the conventional electronic commerce platform. The robot mainly comprises a data collection module, a data processing module, a data fusion module and an address generation module. The data collection module collects commodity information and associated user information, the data processing module cleans and performs characteristic engineering on the collected data, the data fusion module fuses the commodity information and the associated user information by using the BERT model, and the address generation module finally generates a distribution address for the commodity according to the fusion information by using the GPT model.
Description
Technical Field
The application belongs to the field of intersection of electronic commerce and artificial intelligence technology, and particularly relates to workflow allocation and address generation by using an artificial intelligence model. In particular, the application relates to an artificial intelligence-based logistics recognition and distribution robot and a control method thereof.
Background
In the current e-commerce platform, users often need to manually fill out or select delivery addresses already stored in the e-commerce platform when purchasing goods, and several major problems exist in this process. First, manually entering an address is not only time consuming, but can also lead to dispensing problems due to incorrect filling. Second, for a scenario where multiple items may require different shipping addresses, the user needs to repeat the address selection operation, which greatly reduces the shopping experience. For example, a user may wish to deliver office supplies to a corporate address and daily necessities to a residential address. Meanwhile, for a new user of the e-commerce platform, since the platform does not store their possible shipping addresses, the new user has to manually fill in the shipping addresses after purchasing the merchandise.
With the rapid development of artificial intelligence technology, especially the breakthrough in the fields of deep learning and natural language processing, artificial intelligence has achieved remarkable application results in a plurality of industries. Particularly in the e-commerce and logistics industries, the artificial intelligence technology can not only optimize commodity recommendation, but also improve the efficiency of warehouse management and logistics distribution. The algorithm can process a large amount of complex data, automatically solve the problems such as inventory management, demand prediction, optimal path planning and the like, and greatly improve the overall operation efficiency and user experience.
BERT is an advanced natural language processing model that understands data by a bi-directional transducer encoder to obtain deep information contained in the data, which is used in the present application to capture the deep information of the collected data.
GPT is a model with text generation as the main function. Unlike BERT, GPT uses a unidirectional transducer as its encoder and focuses on generating the next text from a given context. The GPT model has powerful natural language generation capabilities, including but not limited to text summarization, machine translation, and natural language dialogue, among other application scenarios. The high flexibility and generating capability make the GPT model excellent in processing various and complex text generating tasks, and the GPT model is used as a model for generating addresses in the application;
in view of the deep expression capability of the BERT and the text generation capability of the GPT, the application aims to automatically generate the delivery address for the user by combining the BERT and the information of the related user, and solves or partially solves the problem that the user needs to manually input or select the delivery address when shopping on an e-commerce platform.
Disclosure of Invention
The application provides a logistics recognition and distribution robot based on artificial intelligence and a control method thereof, which aim to solve the problems in the prior art proposed by the background art.
In order to achieve the above object, the present application provides a logistics recognition and distribution robot based on artificial intelligence, which is characterized in that the robot comprises the following modules:
the data collection module is used for collecting commodity information purchased by a user and associated user information;
the commodity information comprises commodity names, commodity descriptions, commodity prices, commodity comments, commodity scores, commodity categories and commodity discount information;
the related user information comprises a user name, a delivery address, a delivery telephone number and delivery commodity information of the related user, wherein the delivery commodity information comprises commodity names, commodity descriptions, commodity prices, commodity comments, commodity scores, commodity categories and commodity discount information of delivery commodities;
the data collection is completed through the following steps:
step 1: and (3) commodity information collection: the commodity information collection includes capturing name, description, price, comments, scores, categories, and discount information for the commodity purchased by the user from a database or other data source;
step 2: and (3) associated user identification: the associated user identification includes running a specific method to find other users associated with the current user, typically based on commonly purchased goods or cross-referenced contact information;
step 3: associated user information collection: the associated user information collection includes obtaining user names, delivery addresses, delivery telephone numbers of other users associated with the current user from a database or other data source;
step 4: and (5) collecting commodity information of the associated user: the related user commodity information collection comprises the steps of obtaining the name, description, price, comments, scores, categories and discount information of commodities purchased by the related user;
step 5: data integration: the data integration includes integrating all of the collected merchandise information and associated user information into a unified data structure for subsequent processing and analysis.
The data processing module is used for processing the data integrated by the data collecting module;
the processing process comprises the following steps:
step 1: data cleaning: the data cleaning comprises removing or correcting inconsistencies, missing values or obvious erroneous data in the data set;
step 2: text preprocessing: the text preprocessing comprises removing stop words and segmentation words for commodity descriptions, comments and other text fields;
step 3: data characteristic engineering: word2vec is used to generate word embeddings for the above-described word segmentation.
And a data fusion module: the method comprises the steps of fusing commodity information and associated user information together through BERT to obtain deep information;
the fusion steps are as follows:
step 1: BERT model configuration and initialization: the BERT model configuration and initialization comprises the steps of selecting a pre-trained BERT model, configuring model parameters such as the number of layers, the number of hidden units and the number of heads, and then initializing the model;
the method specifically comprises the following steps:
step 1.1: loading a pre-trained BERT model: acquiring a BERT model stored with pre-training weights;
step 1.2: parameter configuration: setting configuration parameters of the BERT model, including learning rate, batch size and optimizer type;
step 1.3: initializing a model: after all necessary parameters are configured, initializing a BERT model;
step 2: embedding words obtained by the data processing module into the BERT model to obtain fusion embedded information fused with commodity information and associated user information;
the method specifically comprises the following steps:
step 2.1: embedding and inputting words of commodity information and associated user information into the BERT model in parallel;
step 2.2: forward propagation calculation is carried out, and a multi-layer transducer network structure of the BERT model is adopted;
step 2.3: and obtaining the fused embedded from the output layer of the BERT model.
The address generation module is used for inputting the fusion vector output by the data fusion module into the trained GPT model and generating different addresses for different commodities purchased by a user;
the address generation process comprises the following steps:
step 1: GPT model preparation and configuration: the GPT model preparation and configuration comprises the steps of selecting a pre-trained GPT model and configuring parameters of the model, including the number of layers, the number of hidden units and the number of heads;
step 2: address generation policy configuration: the address generation policy configuration includes setting constraint conditions of the generated address, such as maximum generation length, generation temperature (controlling the diversity of generated text), and other necessary generation policies;
step 3: address generation performs: the address generation execution includes inputting the fusion vector and the generation policy to a GPT model to generate a final dispatch address.
The application also provides a control method of the logistics recognition and distribution robot based on artificial intelligence, which is characterized by comprising the following steps:
step 1: commodity data and associated user data are collected;
the method specifically comprises the following steps:
step 1.1: and (3) commodity information collection, namely capturing the names, descriptions, prices, comments, scores, categories and discount information of commodities purchased by the user from a database or other data source.
Step 1.2: associated user identification, running a specific method to find other users associated with the current user, typically based on commonly purchased goods or cross-referenced contact information.
Step 1.3: and (3) collecting associated user information, and acquiring the user names, distribution addresses and distribution telephone numbers of other users associated with the current user from a database or other data sources.
Step 1.4: and (5) collecting commodity information of the associated user, and acquiring the name, description, price, comments, scores, categories and discount information of the commodities purchased by the associated user.
Step 1.5: and integrating the data, namely integrating all the collected commodity information and the associated user information into a unified data structure so as to carry out subsequent processing and analysis.
Step 2: processing commodity data and associated user data collected according to the step 1;
the method specifically comprises the following steps:
step 2.1: data cleansing, removing or correcting inconsistencies, missing values, or apparent erroneous data in the data set.
Step 2.2: text preprocessing, namely removing stop words and segmentation words from commodity descriptions, comments and other text fields.
Step 2.3: and (3) data feature engineering, namely generating word embedding of the word segmentation by using word2 vec.
Step 3: fusing the commodity data processed in the step 2 with the associated user number data;
the method specifically comprises the following steps:
step 3.1: the BERT model configuration and initialization, selecting a pre-trained BERT model, configuring model parameters such as the layer number, the number of hidden units and the number of heads, and then initializing the model.
Step 3.2: and data input, namely embedding the words obtained by the data processing module into the BERT model to obtain the fusion embedded product fused with commodity information and associated user information.
Step 4: generating a distribution address for each commodity according to the result of the step 3;
the method specifically comprises the following steps:
step 4.1: GPT model preparation and configuration, selecting a pre-trained GPT model, and configuring parameters of the model, including the number of layers, the number of hidden units and the number of heads.
Step 4.2: the address generation policy configuration sets constraint conditions for generating the address, such as maximum generation length, generation temperature (controlling the diversity of generated text), and other necessary generation policies.
Step 4.3: and executing address generation, inputting the fusion vector and the generation strategy into a GPT model, and generating a final delivery address.
Drawings
FIG. 1 is a flow chart of a method for controlling a logistics recognition and distribution robot based on artificial intelligence.
Detailed Description
The following describes an embodiment of the present application in detail with reference to the drawings and examples, and as shown in fig. 1, the method for controlling a logistics recognition and distribution robot based on artificial intelligence provided by the present application includes the following steps:
step 1: commodity data and associated user data are collected;
the method specifically comprises the following steps:
step 1.1: and (3) commodity information collection, namely capturing the names, descriptions, prices, comments, scores, categories and discount information of commodities purchased by the user from a database or other data source.
Step 1.2: associated user identification, running a specific method to find other users associated with the current user, typically based on commonly purchased goods or cross-referenced contact information; one possible associated user determination method determines an associated user based on the registered contact of the current user. For example, the current user a registers an account of the e-commerce platform through a mobile phone number, and the commodity distribution telephone of the registered user B of the platform comprises the registered mobile phone number of the current user a, then the user B can be determined as an associated user of the current user a;
step 1.3: and (3) collecting associated user information, and acquiring the user names, distribution addresses and distribution telephone numbers of other users associated with the current user from a database or other data sources.
Step 1.4: and (5) collecting commodity information of the associated user, and acquiring the name, description, price, comments, scores, categories and discount information of the commodities purchased by the associated user.
Step 1.5: and integrating the data, namely integrating all the collected commodity information and the associated user information into a unified data structure so as to carry out subsequent processing and analysis.
Step 2: processing commodity data and associated user data collected according to the step 1;
the method specifically comprises the following steps:
step 2.1: data cleansing, removing or correcting inconsistencies, missing values, or apparent erroneous data in the data set.
Step 2.2: text preprocessing, namely removing stop words and segmentation words from commodity descriptions, comments and other text fields.
Step 2.3: and (3) data feature engineering, namely generating word embedding of the word segmentation by using word2 vec.
Step 3: fusing the commodity data processed in the step 2 with the associated user number data;
the method specifically comprises the following steps:
step 3.1: the BERT model configuration and initialization, selecting a pre-trained BERT model, configuring model parameters such as the layer number, the number of hidden units and the number of heads, and then initializing the model.
Step 3.2: and data input, namely embedding the words obtained by the data processing module into the BERT model to obtain the fusion embedded product fused with commodity information and associated user information.
One possible code implementation is as follows:
step 4: generating a distribution address for each commodity according to the result of the step 3;
the method specifically comprises the following steps:
step 4.1: GPT model preparation and configuration, selecting a pre-trained GPT model, and configuring parameters of the model, including the number of layers, the number of hidden units and the number of heads.
Step 4.2: the address generation policy configuration sets constraint conditions for generating the address, such as maximum generation length, generation temperature (controlling the diversity of generated text), and other necessary generation policies.
Step 4.3: and executing address generation, inputting the fusion vector and the generation strategy into a GPT model, and generating a final delivery address.
One possible code is as follows:
compared with the prior art, the application has the following beneficial effects:
the innovation of the present application is mainly embodied in two aspects. First aspect: the BERT and GPT models are utilized to automatically assign different logistics addresses to different commodities of a user. Second,: the information of the commodity and the information of the related user are innovatively fused by using the BERT, and the information is unified into the fusion vector with deep information and is input into the GPT model, so that the GPT model can automatically generate an address for the user without considering the user as a new user or an old user, and the user experience is greatly improved for the new user and the old user.
It should be noted that, in addition to the above-described embodiments, any reasonable modification of the present application should be regarded as being within the scope of the claimed application.
Claims (7)
1. Logistics recognition and distribution robot based on artificial intelligence, which is characterized by comprising the following modules:
the data collection module is used for collecting commodity information purchased by a user and associated user information;
the data processing module is used for processing commodity information and user related information collected by the data collecting module;
the data fusion module is used for fusing the commodity information and the associated user information together through BERT to obtain a fusion vector;
the address generation module is used for inputting the fusion vector output by the data fusion module into the trained GPT model and generating different addresses for different commodities purchased by the user.
2. The artificial intelligence based logistics recognition and distribution robot of claim 1, wherein the commodity information comprises commodity name, commodity description, commodity price, commodity comment, commodity score, commodity category, commodity discount information;
the related user information comprises a user name, a delivery address, a delivery telephone number and delivery commodity information of the related user, wherein the delivery commodity information comprises commodity names, commodity descriptions, commodity prices, commodity comments, commodity scores, commodity categories and commodity discount information of delivery commodities.
3. The artificial intelligence based logistics recognition dispensing robot of claim 1, wherein the data processing module performs data collection by:
step 1: and (3) commodity information collection: the commodity information collection includes capturing name, description, price, comments, scores, categories, and discount information for the commodity purchased by the user from a database or other data source;
step 2: and (3) associated user identification: the associated user identification includes running a specific method to find other users associated with the current user, typically based on commonly purchased goods or cross-referenced contact information;
step 3: associated user information collection: the associated user information collection includes obtaining user names, delivery addresses, delivery telephone numbers of other users associated with the current user from a database or other data source;
step 4: and (5) collecting commodity information of the associated user: the related user commodity information collection comprises the steps of obtaining the name, description, price, comments, scores, categories and discount information of commodities purchased by the related user;
step 5: data integration: the data integration includes integrating all of the collected merchandise information and associated user information into a unified data structure for subsequent processing and analysis.
4. The artificial intelligence based logistics recognition dispensing robot of claim 1, wherein the data processing module process comprises the steps of:
step 1: data cleaning: the data cleaning comprises removing or correcting inconsistencies, missing values or obvious erroneous data in the data set;
step 2: text preprocessing: the text preprocessing comprises removing stop words and segmentation words for commodity descriptions, comments and other text fields;
step 3: data characteristic engineering: word2vec is used to generate word embeddings for the above-described word segmentation.
5. The logistics recognition and distribution robot based on artificial intelligence of claim 1, wherein the step of fusing the data fusion module comprises:
step 1: BERT model configuration and initialization: the BERT model configuration and initialization comprises the steps of selecting a pre-trained BERT model, configuring model parameters such as the number of layers, the number of hidden units and the number of heads, and then initializing the model;
step 2: and embedding the words obtained by the data processing module into the BERT model to obtain the fusion embedded product fused with the commodity information and the associated user information.
6. The artificial intelligence based logistics recognition and distribution robot of claim 1, wherein the address generation module generates the distribution address for the different goods in accordance with the steps of:
step 1: GPT model preparation and configuration: the GPT model preparation and configuration comprises the steps of selecting a pre-trained GPT model and configuring parameters of the model, including the number of layers, the number of hidden units and the number of heads;
step 2: address generation policy configuration: the address generation strategy configuration comprises the steps of setting constraint conditions of the generated address, including maximum generation length, generation temperature and other necessary generation strategies;
step 3: address generation performs: the address generation execution includes inputting the fusion vector and the generation policy to a GPT model to generate a final dispatch address.
7. A logistic recognition and distribution robot control method based on artificial intelligence, characterized in that the robot is the robot according to claim 1, the control method comprising the steps of:
step 1: commodity data and associated user data are collected;
step 2: processing commodity data and associated user data collected according to the step 1;
step 3: fusing the commodity data processed in the step 2 with the associated user number data;
step 4: and generating a delivery address for each commodity according to the result of the step 3.
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